One of the goals of the 2007 NIAAA Call to Action to Prevent and Reduce Underage Drinking is to promote an understanding of college student alcohol consumption in a developmental framework that accounts for individual adolescent characteristics. Toward this end, etiological work in college alcohol prevention has identified several psychosocial variables related to underage drinking including alcohol expectancies, perceived norms, and social influences (Borsari et al., 2007;Read et al., 2005;Sher &Rutledge, 2007). In addition, patterns of heavy alcohol use during the college experience have been established (Del Boca et al., 2004;Goudriaan et al., 2007). However, little is known about how natural patterns of pre-college matriculation psychosocial variables influence students'high-risk drinking at various points during their first year of college. The aims of the current proposal are to examine how pre-college matriculation variables influence college drinking using data from a currently funded NIAAA project (R01 AA015737). The first aim is to use latent profile analysis (LPA) to examine naturally occurring patterns of pre-college psychosocial risk variables. LPA allows for the creation of distinct subgroups of students who are at elevated harm for alcohol abuse based on these psychosocial variables. The second aim is to examine patterns of heavy drinking among first year college students and identify points in time that students are at the most risk for alcohol harm. The third aim is to use a general growth mixture model to examine how the student risk subgroups formed from pre-college variables influence the drinking patterns of students during their freshmen year. The public health relevance of this proposal includes identifying subgroup of students who may be at elevated risk of alcohol-related harm based on pre-college variables. Additionally, the proposed research will identify the different points in time where harm may be the highest for each subgroup during the course of the first year in college. The early identification of at risk students is an integral part of a targeted intervention approach where matching appropriate interventions to participants is viewed as a way to greatly increase intervention efficacy and reduce costs associated with indiscriminate distribution (King et al., 2008.